{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Setting environment and dataset files\n",
    "<a id='load_data'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import environ\n",
    "environ['train_device'] = 'cuda:4' # training device: 'cpu' or 'cuda:X'\n",
    "environ['store_device'] = 'cuda:4' # Data storing device:  'cpu' or 'cuda:X'\n",
    "\n",
    "# dataset_file_MC = '/data/lm4718/datasets/dataset_multicomp_fusion_train_val+batch375001-499939.pkl'\n",
    "dataset_file_MC = '/data/lm4718/datasets/dataset_multicomp_fusion_train_val+batch375001-499939+batch191004-288757.pkl'\n",
    "test_dataset_file_MC = '/data/kk4796/datasets/dataset_multicomp_fusion_test.pkl'\n",
    "benchmark_dataset_file_MC ='/data/mm12191/datasets/benchmarks_ds2.json' \n",
    "\n",
    "train_val_dataset_file_SC = '/data/kk4796/datasets/dataset_singlecomp_13M115_train_val.pkl'\n",
    "test_dataset_file_SC = '/data/kk4796/datasets/dataset_singlecomp_13M115_test.pkl'\n",
    "\n",
    "\n",
    "%run utils_k-best.py # imports and defines some utils functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data\n",
    "<a id='load_data'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 196/196 [00:04<00:00, 44.86it/s]\n",
      "100%|██████████| 14607/14607 [00:18<00:00, 781.11it/s] "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of batches 22\n",
      "Number of batches dropped due to too much memory accesses:642\n",
      "Data loaded\n",
      "Sizes: (22, 0) batches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "#Loading the test dataset\n",
    "%run utils_k-best.py # imports and defines some utils functions\n",
    "test_dataset, test_bl, test_indices, _, _ = load_merge_data(test_dataset_file_MC, test_dataset_file_SC, 1, filter_func_MC=filter_schedule_MC, filter_func_SC=filter_schedule_SC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
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     "text": [
      "F({C0,C1},L3) function396019\n"
     ]
    },
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      " 46%|████▌     | 28694/62650 [00:21<00:26, 1276.08it/s]"
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     "text": [
      "F({C0,C1},L5) function191127\n"
     ]
    },
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      " 46%|████▋     | 29076/62650 [00:22<00:27, 1237.04it/s]"
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     "text": [
      "F({C0,C1},L5) function191589\n"
     ]
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      " 49%|████▉     | 30681/62650 [00:23<00:26, 1221.30it/s]"
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      "F({C0,C1},L4) function193529\n"
     ]
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     "text": [
      "F({C1,C2},L4) function195247\n"
     ]
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     "text": [
      "F({C1,C2},L4) function196553\n"
     ]
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      " 53%|█████▎    | 33379/62650 [00:25<00:24, 1211.44it/s]"
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     "text": [
      "F({C0,C1},L4) function196873\n"
     ]
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      " 55%|█████▍    | 34248/62650 [00:26<00:23, 1218.93it/s]"
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     "text": [
      "F({C0,C1},L4) function198051\n"
     ]
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      " 58%|█████▊    | 36340/62650 [00:28<00:21, 1216.03it/s]"
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     "text": [
      "F({C0,C1},L4) function200647\n",
      "F({C0,C1},L4) function200902\n"
     ]
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     "text": [
      "F({C0,C1},L5) function201020\n"
     ]
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     "text": [
      "F({C0,C1},L5) function201660\n",
      "F({C1,C2},L4) function201818\n",
      "F({C0,C1},L4) function201920\n"
     ]
    },
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      "F({C0,C1},L5) function202224\n"
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     "text": [
      "F({C0,C1},L4) function203706\n",
      "F({C1,C2},L4) function203768\n"
     ]
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      "F({C0,C1},L4) function204062\n"
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      "F({C0,C1},L4) function206051\n"
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      "F({C0,C1},L4) function207152\n"
     ]
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      " 67%|██████▋   | 42134/62650 [00:32<00:16, 1209.93it/s]"
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     "text": [
      "F({C0,C1},L4) function207727\n"
     ]
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      " 68%|██████▊   | 42498/62650 [00:33<00:16, 1195.73it/s]"
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      "F({C0,C1},L5) function208337\n"
     ]
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      "F({C0,C1},L4) function209757\n"
     ]
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     "text": [
      "F({C0,C1},L4) function210271\n"
     ]
    },
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      " 71%|███████   | 44585/62650 [00:35<00:14, 1208.32it/s]"
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     "text": [
      "F({C0,C1},L5) function210891\n"
     ]
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     "text": [
      "F({C0,C1},L4) function211561\n"
     ]
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      " 73%|███████▎  | 45436/62650 [00:35<00:14, 1200.35it/s]"
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     "text": [
      "F({C1,C2},L4) function211994\n"
     ]
    },
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      " 73%|███████▎  | 45922/62650 [00:36<00:13, 1198.83it/s]"
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     "text": [
      "F({C1,C2},L4) function212510\n",
      "F({C0,C1},L4) function212650\n"
     ]
    },
    {
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      "F({C1,C2},L4) function214024\n"
     ]
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      " 76%|███████▋  | 47871/62650 [00:37<00:12, 1214.28it/s]"
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      "F({C1,C2},L4) function215061\n"
     ]
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      "F({C1,C2},L4) function215978\n"
     ]
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      " 78%|███████▊  | 48969/62650 [00:38<00:11, 1189.79it/s]"
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     "text": [
      "F({C1,C2},L4) function216462\n"
     ]
    },
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      " 79%|███████▉  | 49458/62650 [00:39<00:10, 1204.24it/s]"
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     "text": [
      "F({C0,C1},L5) function217102\n"
     ]
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     "text": [
      "F({C0,C1},L4) function217785\n",
      "F({C0,C1},L4) function217900\n"
     ]
    },
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     "output_type": "stream",
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     "text": [
      "F({C0,C1},L3) function218093\n"
     ]
    },
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      " 81%|████████  | 50799/62650 [00:40<00:09, 1205.73it/s]"
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     "text": [
      "F({C0,C1},L4) function218687\n",
      "F({C0,C1},L4) function218843\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "F({C0,C1},L4) function218994\n",
      "F({C0,C1},L3) function219209\n"
     ]
    },
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "F({C0,C1},L5) function219597\n",
      "F({C0,C1},L5) function219716\n"
     ]
    },
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     "output_type": "stream",
     "text": [
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     "output_type": "stream",
     "text": [
      "F({C0,C1},L4) function222162\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 86%|████████▌ | 53861/62650 [00:42<00:07, 1204.51it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F({C0,C1},L4) function222739\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 62650/62650 [00:49<00:00, 1267.45it/s]\n",
      "100%|██████████| 131466/131466 [03:15<00:00, 672.38it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of batches 457\n",
      "Number of batches dropped due to too much memory accesses:8833\n",
      "Data loaded\n",
      "Sizes: (91, 366) batches\n"
     ]
    }
   ],
   "source": [
    "#Loading the train/val dataset\n",
    "train_val_dataset, val_bl, val_indices, train_bl, train_indices = load_merge_data(dataset_file_MC, train_val_dataset_file_SC, 0.2, max_batch_size=832, filter_func_MC=filter_schedule_MC, filter_func_SC=filter_schedule_SC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "457"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#total batches number\n",
    "len(train_val_dataset.X)\n",
    "#356"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([54, 2, 970]) <BL0<BL1<BL2[CI0CI1]EL2>EL1>EL0>\n",
      "torch.Size([28, 2, 970]) <BL0<BL1<BL2[CI0]EL2><BL3[CI1]EL3>EL1>EL0>\n",
      "torch.Size([4, 2, 970]) <BL0<BL1<BL2<BL3[CI0]EL3><BL4[CI1]EL4>EL2>EL1>EL0>\n",
      "torch.Size([83, 2, 970]) <BL0<BL1[CI0CI1]EL1>EL0>\n",
      "torch.Size([5, 2, 970]) <BL0<BL1<BL2<BL3[CI0]EL3>EL2><BL4[CI1]EL4>EL1>EL0>\n",
      "torch.Size([1, 2, 970]) <BL0<BL1<BL2<BL3[CI0]EL3><BL4<BL5[CI1]EL5>EL4>EL2>EL1>EL0>\n",
      "torch.Size([3, 2, 970]) <BL0<BL1<BL2<BL3[CI0CI1]EL3>EL2>EL1>EL0>\n",
      "torch.Size([7, 2, 970]) <BL0<BL1<BL2[CI0]EL2><BL3<BL4[CI1]EL4>EL3>EL1>EL0>\n",
      "torch.Size([1, 2, 970]) <BL0<BL1<BL2<BL3<BL4[CI0CI1]EL4>EL3>EL2>EL1>EL0>\n",
      "torch.Size([1, 2, 970]) <BL0<BL1<BL2<BL3<BL4[CI0]EL4>EL3><BL5[CI1]EL5>EL2>EL1>EL0>\n",
      "torch.Size([1, 2, 970]) <BL0<BL1<BL2<BL3<BL4[CI0]EL4>EL3>EL2><BL5[CI1]EL5>EL1>EL0>\n",
      "torch.Size([1, 2, 970]) <BL0<BL1<BL2<BL3[CI0]EL3>EL2><BL4<BL5<BL6[CI1]EL6>EL5>EL4>EL1>EL0>\n",
      "torch.Size([2048, 1, 970]) <BL0<BL1<BL2[CI0]EL2>EL1>EL0>\n",
      "torch.Size([2048, 1, 970]) <BL0<BL1<BL2[CI0]EL2>EL1>EL0>\n",
      "torch.Size([2048, 1, 970]) <BL0<BL1<BL2[CI0]EL2>EL1>EL0>\n",
      "torch.Size([2048, 1, 970]) <BL0<BL1<BL2[CI0]EL2>EL1>EL0>\n",
      "torch.Size([1878, 1, 970]) <BL0<BL1<BL2[CI0]EL2>EL1>EL0>\n",
      "torch.Size([1406, 1, 970]) <BL0<BL1<BL2<BL3[CI0]EL3>EL2>EL1>EL0>\n",
      "torch.Size([2048, 1, 970]) <BL0<BL1[CI0]EL1>EL0>\n",
      "torch.Size([304, 1, 970]) <BL0<BL1[CI0]EL1>EL0>\n",
      "torch.Size([139, 1, 970]) <BL0<BL1<BL2<BL3<BL4[CI0]EL4>EL3>EL2>EL1>EL0>\n",
      "torch.Size([5, 1, 970]) <BL0<BL1<BL2<BL3<BL4<BL5[CI0]EL5>EL4>EL3>EL2>EL1>EL0>\n"
     ]
    }
   ],
   "source": [
    "#batches sizes and tree structure\n",
    "for i in range(len(test_dataset.X)):\n",
    "    print(test_dataset.X[i][1].size(), get_tree_footprint(test_dataset.X[i][0]) )  \n",
    "    #format of first output: batch size, number of computations, size of each comps vector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %run utils_k-best.py\n",
    "\n",
    "# input_size = 1272\n",
    "input_size = 970 #Before adding fusion, it was: 963\n",
    "\n",
    "model = None \n",
    "\n",
    "model = Model_Recursive_LSTM_v2(input_size,comp_embed_layer_sizes=[600, 900, 600, 400, 200], drops=[0.275, 0.4, 0.275, 0.175, 0.175], output_size=one_output * 5)\n",
    "\n",
    "model.to(train_device) \n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "optimizer = AdamW(model.parameters(),weight_decay=0.375e-2)  #0.375e-2\n",
    "# optimizer = optim.RMSprop(model.parameters(), weight_decay=0.3e-2) # Other tested optimizer\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500:  train Loss: 272.4278   val Loss: 266.1727   time: 3.96s   best: 266.1727\n",
      "Epoch 2/500:  train Loss: 270.7854   val Loss: 265.7120   time: 3.96s   best: 265.7120\n",
      "Epoch 3/500:  train Loss: 271.3999   val Loss: 267.0474   time: 3.96s   best: 265.7120\n",
      "Epoch 4/500:  train Loss: 271.5244   val Loss: 265.5790   time: 3.97s   best: 265.5790\n",
      "Epoch 5/500:  train Loss: 272.1784   val Loss: 268.5279   time: 3.97s   best: 265.5790\n",
      "Epoch 6/500:  train Loss: 271.0595   val Loss: 266.2275   time: 3.97s   best: 265.5790\n",
      "Epoch 7/500:  train Loss: 274.1678   val Loss: 266.9583   time: 3.97s   best: 265.5790\n",
      "Epoch 8/500:  train Loss: 271.1928   val Loss: 267.0074   time: 4.03s   best: 265.5790\n",
      "Epoch 9/500:  train Loss: 271.6989   val Loss: 266.9711   time: 4.13s   best: 265.5790\n",
      "Epoch 10/500:  train Loss: 273.5782   val Loss: 267.5128   time: 3.97s   best: 265.5790\n",
      "Epoch 11/500:  train Loss: 270.6140   val Loss: 266.3777   time: 3.97s   best: 265.5790\n",
      "Epoch 12/500:  train Loss: 270.2995   val Loss: 267.0115   time: 3.97s   best: 265.5790\n",
      "Epoch 13/500:  train Loss: 271.5449   val Loss: 266.4434   time: 3.97s   best: 265.5790\n",
      "Epoch 14/500:  train Loss: 277.8685   val Loss: 279.2951   time: 3.97s   best: 265.5790\n",
      "Epoch 15/500:  train Loss: 287.3158   val Loss: 268.7563   time: 3.97s   best: 265.5790\n",
      "Epoch 16/500:  train Loss: 273.9218   val Loss: 267.1796   time: 3.97s   best: 265.5790\n",
      "Epoch 17/500:  train Loss: 272.2946   val Loss: 266.1779   time: 3.97s   best: 265.5790\n",
      "Epoch 18/500:  train Loss: 273.8303   val Loss: 266.3290   time: 3.97s   best: 265.5790\n",
      "Epoch 19/500:  train Loss: 270.9177   val Loss: 265.4652   time: 3.97s   best: 265.4652\n",
      "Epoch 20/500:  train Loss: 270.2010   val Loss: 266.0224   time: 3.96s   best: 265.4652\n",
      "Epoch 21/500:  train Loss: 270.5055   val Loss: 266.1900   time: 3.96s   best: 265.4652\n",
      "Epoch 22/500:  train Loss: 270.3608   val Loss: 266.7723   time: 3.96s   best: 265.4652\n",
      "Epoch 23/500:  train Loss: 270.8866   val Loss: 266.4377   time: 3.97s   best: 265.4652\n",
      "Epoch 24/500:  train Loss: 270.1132   val Loss: 265.0625   time: 3.97s   best: 265.0625\n",
      "Epoch 25/500:  train Loss: 271.4073   val Loss: 266.9486   time: 3.97s   best: 265.0625\n",
      "Epoch 26/500:  train Loss: 270.0983   val Loss: 265.6880   time: 4.02s   best: 265.0625\n",
      "Epoch 27/500:  train Loss: 271.8310   val Loss: 267.2210   time: 3.99s   best: 265.0625\n",
      "Epoch 28/500:  train Loss: 271.8648   val Loss: 265.2472   time: 3.98s   best: 265.0625\n",
      "Epoch 29/500:  train Loss: 271.6719   val Loss: 265.5328   time: 3.98s   best: 265.0625\n",
      "Epoch 30/500:  train Loss: 270.5783   val Loss: 265.8183   time: 3.97s   best: 265.0625\n",
      "Epoch 31/500:  train Loss: 270.4977   val Loss: 266.5495   time: 3.97s   best: 265.0625\n",
      "Epoch 32/500:  train Loss: 270.1897   val Loss: 266.6078   time: 3.98s   best: 265.0625\n",
      "Epoch 33/500:  train Loss: 270.9489   val Loss: 266.4107   time: 4.03s   best: 265.0625\n",
      "Epoch 34/500:  train Loss: 271.7650   val Loss: 264.9468   time: 3.98s   best: 264.9468\n",
      "Epoch 35/500:  train Loss: 271.5221   val Loss: 266.4886   time: 3.98s   best: 264.9468\n",
      "Epoch 36/500:  train Loss: 270.7052   val Loss: 266.4057   time: 3.97s   best: 264.9468\n",
      "Epoch 37/500:  train Loss: 270.3718   val Loss: 265.9107   time: 3.97s   best: 264.9468\n",
      "Epoch 38/500:  train Loss: 273.0658   val Loss: 284.5662   time: 3.96s   best: 264.9468\n",
      "Epoch 39/500:  train Loss: 275.1248   val Loss: 266.9282   time: 4.03s   best: 264.9468\n",
      "Epoch 40/500:  train Loss: 273.2767   val Loss: 268.1360   time: 3.97s   best: 264.9468\n",
      "Epoch 41/500:  train Loss: 270.7760   val Loss: 268.0612   time: 3.98s   best: 264.9468\n",
      "Epoch 42/500:  train Loss: 271.5078   val Loss: 266.5270   time: 3.97s   best: 264.9468\n",
      "Epoch 43/500:  train Loss: 275.2575   val Loss: 265.1952   time: 3.97s   best: 264.9468\n",
      "Epoch 44/500:  train Loss: 271.0905   val Loss: 267.9472   time: 3.96s   best: 264.9468\n",
      "Epoch 45/500:  train Loss: 279.8442   val Loss: 279.6796   time: 3.99s   best: 264.9468\n",
      "Epoch 46/500:  train Loss: 275.0737   val Loss: 266.6891   time: 4.01s   best: 264.9468\n",
      "Epoch 47/500:  train Loss: 271.5943   val Loss: 266.4566   time: 3.98s   best: 264.9468\n",
      "Epoch 48/500:  train Loss: 269.8504   val Loss: 266.6003   time: 3.98s   best: 264.9468\n",
      "Epoch 49/500:  train Loss: 270.1565   val Loss: 266.9730   time: 3.97s   best: 264.9468\n",
      "Epoch 50/500:  train Loss: 271.0965   val Loss: 266.8226   time: 3.97s   best: 264.9468\n",
      "Epoch 51/500:  train Loss: 272.6584   val Loss: 265.7811   time: 3.96s   best: 264.9468\n",
      "Epoch 52/500:  train Loss: 269.8940   val Loss: 267.1634   time: 4.02s   best: 264.9468\n",
      "Epoch 53/500:  train Loss: 269.8039   val Loss: 273.6302   time: 3.97s   best: 264.9468\n",
      "Epoch 54/500:  train Loss: 270.4914   val Loss: 267.6606   time: 3.97s   best: 264.9468\n",
      "Epoch 55/500:  train Loss: 271.5368   val Loss: 265.8068   time: 3.97s   best: 264.9468\n",
      "Epoch 56/500:  train Loss: 278.2563   val Loss: 267.2397   time: 3.96s   best: 264.9468\n",
      "Epoch 57/500:  train Loss: 272.3223   val Loss: 265.2951   time: 3.96s   best: 264.9468\n",
      "Epoch 58/500:  train Loss: 273.2288   val Loss: 268.0580   time: 4.01s   best: 264.9468\n",
      "Epoch 59/500:  train Loss: 285.7529   val Loss: 267.3384   time: 3.98s   best: 264.9468\n",
      "Epoch 60/500:  train Loss: 273.3054   val Loss: 266.6147   time: 3.97s   best: 264.9468\n",
      "Epoch 61/500:  train Loss: 276.7045   val Loss: 267.4019   time: 3.97s   best: 264.9468\n",
      "Epoch 62/500:  train Loss: 272.0513   val Loss: 265.5510   time: 3.96s   best: 264.9468\n",
      "Epoch 63/500:  train Loss: 271.0723   val Loss: 266.8576   time: 3.96s   best: 264.9468\n",
      "Epoch 64/500:  train Loss: 270.3262   val Loss: 265.2456   time: 3.98s   best: 264.9468\n",
      "Epoch 65/500:  train Loss: 270.3436   val Loss: 264.6596   time: 4.01s   best: 264.6596\n",
      "Epoch 66/500:  train Loss: 269.5150   val Loss: 266.2051   time: 3.98s   best: 264.6596\n",
      "Epoch 67/500:  train Loss: 268.9606   val Loss: 266.1112   time: 3.98s   best: 264.6596\n",
      "Epoch 68/500:  train Loss: 268.9420   val Loss: 264.4953   time: 3.97s   best: 264.4953\n",
      "Epoch 69/500:  train Loss: 269.1066   val Loss: 266.0235   time: 3.97s   best: 264.4953\n",
      "Epoch 70/500:  train Loss: 269.0426   val Loss: 266.6453   time: 3.96s   best: 264.4953\n",
      "Epoch 71/500:  train Loss: 270.5462   val Loss: 266.2742   time: 4.03s   best: 264.4953\n",
      "Epoch 72/500:  train Loss: 269.7626   val Loss: 268.1540   time: 3.97s   best: 264.4953\n",
      "Epoch 73/500:  train Loss: 270.3317   val Loss: 266.4777   time: 3.97s   best: 264.4953\n",
      "Epoch 74/500:  train Loss: 270.4685   val Loss: 267.6538   time: 3.97s   best: 264.4953\n",
      "Epoch 75/500:  train Loss: 270.5449   val Loss: 266.5450   time: 3.96s   best: 264.4953\n",
      "Epoch 76/500:  train Loss: 269.6602   val Loss: 265.6184   time: 3.96s   best: 264.4953\n",
      "Epoch 77/500:  train Loss: 270.0857   val Loss: 266.6148   time: 4.00s   best: 264.4953\n",
      "Epoch 78/500:  train Loss: 269.6258   val Loss: 265.7644   time: 3.99s   best: 264.4953\n",
      "Epoch 79/500:  train Loss: 269.7248   val Loss: 267.8196   time: 3.98s   best: 264.4953\n",
      "Epoch 80/500:  train Loss: 271.7473   val Loss: 270.1842   time: 3.97s   best: 264.4953\n",
      "Epoch 81/500:  train Loss: 269.5682   val Loss: 265.5799   time: 3.96s   best: 264.4953\n",
      "Epoch 82/500:  train Loss: 268.8453   val Loss: 266.8850   time: 3.97s   best: 264.4953\n",
      "Epoch 83/500:  train Loss: 271.8859   val Loss: 268.0385   time: 3.96s   best: 264.4953\n",
      "Epoch 84/500:  train Loss: 269.4399   val Loss: 264.3512   time: 4.02s   best: 264.3512\n",
      "Epoch 85/500:  train Loss: 268.2635   val Loss: 265.8738   time: 3.98s   best: 264.3512\n",
      "Epoch 86/500:  train Loss: 269.8269   val Loss: 266.6396   time: 3.98s   best: 264.3512\n",
      "Epoch 87/500:  train Loss: 270.7725   val Loss: 266.4530   time: 3.97s   best: 264.3512\n",
      "Epoch 88/500:  train Loss: 269.4358   val Loss: 268.0136   time: 3.97s   best: 264.3512\n",
      "Epoch 89/500:  train Loss: 269.7051   val Loss: 265.1414   time: 3.97s   best: 264.3512\n",
      "Epoch 90/500:  train Loss: 269.9920   val Loss: 264.7338   time: 4.02s   best: 264.3512\n",
      "Epoch 91/500:  train Loss: 271.8304   val Loss: 266.6847   time: 3.99s   best: 264.3512\n",
      "Epoch 92/500:  train Loss: 270.1278   val Loss: 266.5669   time: 3.98s   best: 264.3512\n",
      "Epoch 93/500:  train Loss: 270.1375   val Loss: 266.6768   time: 3.98s   best: 264.3512\n",
      "Epoch 94/500:  train Loss: 269.6130   val Loss: 265.9523   time: 3.97s   best: 264.3512\n",
      "Epoch 95/500:  train Loss: 268.5876   val Loss: 265.0761   time: 3.97s   best: 264.3512\n",
      "Epoch 96/500:  train Loss: 268.4992   val Loss: 267.2556   time: 3.98s   best: 264.3512\n",
      "Epoch 97/500:  train Loss: 269.1661   val Loss: 266.0046   time: 4.03s   best: 264.3512\n",
      "Epoch 98/500:  train Loss: 273.1851   val Loss: 265.4686   time: 3.99s   best: 264.3512\n",
      "Epoch 99/500:  train Loss: 269.9769   val Loss: 269.9235   time: 3.98s   best: 264.3512\n",
      "Epoch 100/500:  train Loss: 271.2463   val Loss: 266.4445   time: 3.98s   best: 264.3512\n",
      "Epoch 101/500:  train Loss: 270.3548   val Loss: 266.1145   time: 3.97s   best: 264.3512\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 102/500:  train Loss: 270.0923   val Loss: 266.4212   time: 3.97s   best: 264.3512\n",
      "Epoch 103/500:  train Loss: 267.7361   val Loss: 266.9136   time: 4.03s   best: 264.3512\n",
      "Epoch 104/500:  train Loss: 268.7107   val Loss: 265.4049   time: 3.98s   best: 264.3512\n",
      "Epoch 105/500:  train Loss: 268.0704   val Loss: 266.2269   time: 3.98s   best: 264.3512\n",
      "Epoch 106/500:  train Loss: 268.8553   val Loss: 265.8094   time: 3.98s   best: 264.3512\n",
      "Epoch 107/500:  train Loss: 268.9571   val Loss: 266.5928   time: 3.97s   best: 264.3512\n",
      "Epoch 108/500:  train Loss: 269.5606   val Loss: 271.3600   time: 3.97s   best: 264.3512\n",
      "Epoch 109/500:  train Loss: 273.4698   val Loss: 274.4793   time: 3.98s   best: 264.3512\n",
      "Epoch 110/500:  train Loss: 271.8558   val Loss: 266.1841   time: 4.01s   best: 264.3512\n",
      "Epoch 111/500:  train Loss: 269.5238   val Loss: 265.5799   time: 3.98s   best: 264.3512\n",
      "Epoch 112/500:  train Loss: 269.7757   val Loss: 265.0213   time: 3.97s   best: 264.3512\n",
      "Epoch 113/500:  train Loss: 268.4185   val Loss: 266.2160   time: 3.97s   best: 264.3512\n",
      "Epoch 114/500:  train Loss: 268.3274   val Loss: 266.0114   time: 3.97s   best: 264.3512\n",
      "Epoch 115/500:  train Loss: 268.9045   val Loss: 267.9575   time: 3.96s   best: 264.3512\n",
      "Epoch 116/500:  train Loss: 271.6162   val Loss: 266.9391   time: 4.03s   best: 264.3512\n",
      "Epoch 117/500:  train Loss: 268.7287   val Loss: 264.4868   time: 3.98s   best: 264.3512\n",
      "Epoch 118/500:  train Loss: 269.8147   val Loss: 266.5459   time: 3.98s   best: 264.3512\n",
      "Epoch 119/500:  train Loss: 267.9620   val Loss: 265.1127   time: 3.97s   best: 264.3512\n",
      "Epoch 120/500:  train Loss: 268.5869   val Loss: 265.2235   time: 3.97s   best: 264.3512\n",
      "Epoch 121/500:  train Loss: 267.8558   val Loss: 267.2972   time: 3.97s   best: 264.3512\n",
      "Epoch 122/500:  train Loss: 268.1592   val Loss: 265.3502   time: 4.02s   best: 264.3512\n",
      "Epoch 123/500:  train Loss: 268.5521   val Loss: 266.4280   time: 3.99s   best: 264.3512\n",
      "Epoch 124/500:  train Loss: 268.1042   val Loss: 264.9276   time: 3.98s   best: 264.3512\n",
      "Epoch 125/500:  train Loss: 268.2770   val Loss: 265.9426   time: 3.98s   best: 264.3512\n",
      "Epoch 126/500:  train Loss: 274.3000   val Loss: 265.0286   time: 3.97s   best: 264.3512\n",
      "Epoch 127/500:  train Loss: 268.8223   val Loss: 264.6873   time: 3.97s   best: 264.3512\n",
      "Epoch 128/500:  train Loss: 268.0006   val Loss: 267.9993   time: 3.98s   best: 264.3512\n",
      "Epoch 129/500:  train Loss: 267.8961   val Loss: 266.2660   time: 4.02s   best: 264.3512\n",
      "Epoch 130/500:  train Loss: 269.1157   val Loss: 267.4007   time: 3.98s   best: 264.3512\n",
      "Epoch 131/500:  train Loss: 267.9274   val Loss: 265.8067   time: 3.98s   best: 264.3512\n",
      "Epoch 132/500:  train Loss: 267.9234   val Loss: 265.3167   time: 3.97s   best: 264.3512\n",
      "Epoch 133/500:  train Loss: 267.6849   val Loss: 265.6430   time: 3.97s   best: 264.3512\n",
      "Epoch 134/500:  train Loss: 267.0563   val Loss: 265.0672   time: 3.96s   best: 264.3512\n",
      "Epoch 135/500:  train Loss: 269.5876   val Loss: 265.5290   time: 4.02s   best: 264.3512\n",
      "Epoch 136/500:  train Loss: 270.3731   val Loss: 265.4077   time: 3.98s   best: 264.3512\n",
      "Epoch 137/500:  train Loss: 267.8825   val Loss: 264.7297   time: 3.98s   best: 264.3512\n",
      "Epoch 138/500:  train Loss: 268.7049   val Loss: 266.0692   time: 3.97s   best: 264.3512\n",
      "Epoch 139/500:  train Loss: 267.7526   val Loss: 265.8034   time: 3.96s   best: 264.3512\n",
      "Epoch 140/500:  train Loss: 271.7058   val Loss: 265.3404   time: 3.96s   best: 264.3512\n",
      "Epoch 141/500:  train Loss: 267.5363   val Loss: 265.3033   time: 3.99s   best: 264.3512\n",
      "Epoch 142/500:  train Loss: 267.7787   val Loss: 265.3639   time: 4.01s   best: 264.3512\n",
      "Epoch 143/500:  train Loss: 267.6958   val Loss: 266.1992   time: 3.98s   best: 264.3512\n",
      "Epoch 144/500:  train Loss: 268.6311   val Loss: 265.9619   time: 3.98s   best: 264.3512\n",
      "Epoch 145/500:  train Loss: 272.2441   val Loss: 266.0976   time: 3.97s   best: 264.3512\n",
      "Epoch 146/500:  train Loss: 267.7826   val Loss: 266.7370   time: 3.97s   best: 264.3512\n",
      "Epoch 147/500:  train Loss: 267.3672   val Loss: 264.9455   time: 3.97s   best: 264.3512\n",
      "Epoch 148/500:  train Loss: 269.3352   val Loss: 266.5001   time: 4.03s   best: 264.3512\n",
      "Epoch 149/500:  train Loss: 266.5004   val Loss: 266.3527   time: 3.98s   best: 264.3512\n",
      "Epoch 150/500:  train Loss: 266.4485   val Loss: 265.2044   time: 3.98s   best: 264.3512\n",
      "Epoch 151/500:  train Loss: 266.2663   val Loss: 266.7947   time: 3.97s   best: 264.3512\n",
      "Epoch 152/500:  train Loss: 267.7507   val Loss: 263.9767   time: 3.97s   best: 263.9767\n",
      "Epoch 153/500:  train Loss: 269.9625   val Loss: 267.9946   time: 3.97s   best: 263.9767\n",
      "Epoch 154/500:  train Loss: 270.8076   val Loss: 265.4473   time: 4.02s   best: 263.9767\n",
      "Epoch 155/500:  train Loss: 267.2002   val Loss: 263.9833   time: 3.99s   best: 263.9767\n",
      "Epoch 156/500:  train Loss: 266.7810   val Loss: 265.4913   time: 3.98s   best: 263.9767\n",
      "Epoch 157/500:  train Loss: 266.8031   val Loss: 263.3371   time: 3.98s   best: 263.3371\n",
      "Epoch 158/500:  train Loss: 267.6067   val Loss: 265.4931   time: 3.97s   best: 263.3371\n",
      "Epoch 159/500:  train Loss: 266.6609   val Loss: 265.2419   time: 3.97s   best: 263.3371\n",
      "Epoch 160/500:  train Loss: 268.4199   val Loss: 265.5728   time: 3.98s   best: 263.3371\n",
      "Epoch 161/500:  train Loss: 269.5456   val Loss: 266.7161   time: 4.02s   best: 263.3371\n",
      "Epoch 162/500:  train Loss: 268.7031   val Loss: 267.9903   time: 3.98s   best: 263.3371\n",
      "Epoch 163/500:  train Loss: 268.1786   val Loss: 265.4508   time: 3.98s   best: 263.3371\n",
      "Epoch 164/500:  train Loss: 266.9733   val Loss: 264.6694   time: 3.97s   best: 263.3371\n",
      "Epoch 165/500:  train Loss: 266.4550   val Loss: 265.9728   time: 3.97s   best: 263.3371\n",
      "Epoch 166/500:  train Loss: 266.1231   val Loss: 266.3657   time: 3.96s   best: 263.3371\n",
      "Epoch 167/500:  train Loss: 267.6614   val Loss: 264.6306   time: 4.03s   best: 263.3371\n",
      "Epoch 168/500:  train Loss: 266.4620   val Loss: 263.7433   time: 3.98s   best: 263.3371\n",
      "Epoch 169/500:  train Loss: 265.9256   val Loss: 264.3677   time: 3.98s   best: 263.3371\n",
      "Epoch 170/500:  train Loss: 268.5205   val Loss: 265.7563   time: 3.97s   best: 263.3371\n",
      "Epoch 171/500:  train Loss: 266.5522   val Loss: 266.0291   time: 3.97s   best: 263.3371\n",
      "Epoch 172/500:  train Loss: 265.2595   val Loss: 266.0492   time: 3.98s   best: 263.3371\n",
      "Epoch 173/500:  train Loss: 265.6839   val Loss: 265.6526   time: 4.01s   best: 263.3371\n",
      "Epoch 174/500:  train Loss: 266.3427   val Loss: 266.4536   time: 4.01s   best: 263.3371\n",
      "Epoch 175/500:  train Loss: 265.8218   val Loss: 264.8435   time: 3.99s   best: 263.3371\n",
      "Epoch 176/500:  train Loss: 265.6890   val Loss: 266.1522   time: 3.98s   best: 263.3371\n",
      "Epoch 177/500:  train Loss: 265.8635   val Loss: 266.1504   time: 3.97s   best: 263.3371\n",
      "Epoch 178/500:  train Loss: 269.9164   val Loss: 265.6253   time: 3.97s   best: 263.3371\n",
      "Epoch 179/500:  train Loss: 269.9402   val Loss: 268.7532   time: 3.96s   best: 263.3371\n",
      "Epoch 180/500:  train Loss: 273.9878   val Loss: 265.0177   time: 4.03s   best: 263.3371\n",
      "Epoch 181/500:  train Loss: 267.0328   val Loss: 275.6499   time: 3.98s   best: 263.3371\n",
      "Epoch 182/500:  train Loss: 270.9278   val Loss: 264.5317   time: 3.98s   best: 263.3371\n",
      "Epoch 183/500:  train Loss: 265.9346   val Loss: 264.5144   time: 3.98s   best: 263.3371\n",
      "Epoch 184/500:  train Loss: 265.8810   val Loss: 263.8069   time: 3.97s   best: 263.3371\n",
      "Epoch 185/500:  train Loss: 266.2110   val Loss: 264.8327   time: 3.96s   best: 263.3371\n",
      "Epoch 186/500:  train Loss: 267.0948   val Loss: 265.4585   time: 4.02s   best: 263.3371\n",
      "Epoch 187/500:  train Loss: 266.7424   val Loss: 266.3638   time: 3.98s   best: 263.3371\n",
      "Epoch 188/500:  train Loss: 267.9803   val Loss: 264.4300   time: 3.98s   best: 263.3371\n",
      "Epoch 189/500:  train Loss: 266.5071   val Loss: 264.2496   time: 3.98s   best: 263.3371\n",
      "Epoch 190/500:  train Loss: 267.0354   val Loss: 264.5960   time: 3.96s   best: 263.3371\n",
      "Epoch 191/500:  train Loss: 264.9404   val Loss: 264.8633   time: 3.97s   best: 263.3371\n",
      "Epoch 192/500:  train Loss: 264.9533   val Loss: 264.3664   time: 3.99s   best: 263.3371\n",
      "Epoch 193/500:  train Loss: 269.3207   val Loss: 264.6142   time: 4.01s   best: 263.3371\n",
      "Epoch 194/500:  train Loss: 265.5860   val Loss: 264.8081   time: 3.98s   best: 263.3371\n",
      "Epoch 195/500:  train Loss: 265.5375   val Loss: 265.9139   time: 3.98s   best: 263.3371\n",
      "Epoch 196/500:  train Loss: 265.9870   val Loss: 264.4501   time: 3.96s   best: 263.3371\n",
      "Epoch 197/500:  train Loss: 267.9052   val Loss: 265.8201   time: 3.97s   best: 263.3371\n",
      "Epoch 198/500:  train Loss: 267.1009   val Loss: 265.0430   time: 3.96s   best: 263.3371\n",
      "Epoch 199/500:  train Loss: 265.4469   val Loss: 266.3005   time: 4.03s   best: 263.3371\n",
      "Epoch 200/500:  train Loss: 265.6193   val Loss: 266.9404   time: 3.98s   best: 263.3371\n",
      "Epoch 201/500:  train Loss: 266.7769   val Loss: 266.8956   time: 3.98s   best: 263.3371\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 202/500:  train Loss: 270.5232   val Loss: 265.8636   time: 3.97s   best: 263.3371\n",
      "Epoch 203/500:  train Loss: 267.6646   val Loss: 265.3150   time: 3.98s   best: 263.3371\n",
      "Epoch 204/500:  train Loss: 267.6697   val Loss: 265.2988   time: 3.97s   best: 263.3371\n",
      "Epoch 205/500:  train Loss: 266.8489   val Loss: 264.3468   time: 4.02s   best: 263.3371\n",
      "Epoch 206/500:  train Loss: 264.9447   val Loss: 263.2504   time: 4.00s   best: 263.2504\n",
      "Epoch 207/500:  train Loss: 265.9953   val Loss: 265.7581   time: 3.99s   best: 263.2504\n",
      "Epoch 208/500:  train Loss: 282.8283   val Loss: 283.9195   time: 3.98s   best: 263.2504\n",
      "Epoch 209/500:  train Loss: 274.5334   val Loss: 264.7824   time: 3.97s   best: 263.2504\n",
      "Epoch 210/500:  train Loss: 271.5287   val Loss: 265.8302   time: 3.97s   best: 263.2504\n",
      "Epoch 211/500:  train Loss: 267.6022   val Loss: 264.9490   time: 3.97s   best: 263.2504\n",
      "Epoch 212/500:  train Loss: 266.4295   val Loss: 275.0589   time: 4.03s   best: 263.2504\n",
      "Epoch 213/500:  train Loss: 267.6192   val Loss: 266.3002   time: 3.98s   best: 263.2504\n",
      "Epoch 214/500:  train Loss: 266.4055   val Loss: 264.5763   time: 3.98s   best: 263.2504\n",
      "Epoch 215/500:  train Loss: 265.0581   val Loss: 264.6347   time: 3.98s   best: 263.2504\n",
      "Epoch 216/500:  train Loss: 264.7698   val Loss: 265.2449   time: 3.96s   best: 263.2504\n",
      "Epoch 217/500:  train Loss: 265.6272   val Loss: 264.2895   time: 3.96s   best: 263.2504\n",
      "Epoch 218/500:  train Loss: 265.2005   val Loss: 263.8891   time: 4.01s   best: 263.2504\n",
      "Epoch 219/500:  train Loss: 264.6580   val Loss: 265.6909   time: 3.99s   best: 263.2504\n",
      "Epoch 220/500:  train Loss: 265.0293   val Loss: 265.3540   time: 3.98s   best: 263.2504\n",
      "Epoch 221/500:  train Loss: 264.1232   val Loss: 263.1782   time: 3.97s   best: 263.1782\n",
      "Epoch 222/500:  train Loss: 266.3043   val Loss: 264.5462   time: 3.97s   best: 263.1782\n",
      "Epoch 223/500:  train Loss: 265.4172   val Loss: 263.3863   time: 3.97s   best: 263.1782\n",
      "Epoch 224/500:  train Loss: 268.6028   val Loss: 263.7216   time: 3.97s   best: 263.1782\n",
      "Epoch 225/500:  train Loss: 265.5121   val Loss: 264.0735   time: 4.03s   best: 263.1782\n",
      "Epoch 226/500:  train Loss: 265.5759   val Loss: 265.5218   time: 3.98s   best: 263.1782\n",
      "Epoch 227/500:  train Loss: 265.6349   val Loss: 265.5250   time: 3.98s   best: 263.1782\n",
      "Epoch 228/500:  train Loss: 265.8185   val Loss: 264.7056   time: 3.97s   best: 263.1782\n",
      "Epoch 229/500:  train Loss: 264.9807   val Loss: 264.0947   time: 3.97s   best: 263.1782\n",
      "Epoch 230/500:  train Loss: 265.3988   val Loss: 264.6180   time: 3.97s   best: 263.1782\n",
      "Epoch 231/500:  train Loss: 272.1232   val Loss: 264.0744   time: 4.02s   best: 263.1782\n",
      "Epoch 232/500:  train Loss: 267.9594   val Loss: 264.4107   time: 3.99s   best: 263.1782\n",
      "Epoch 233/500:  train Loss: 265.0412   val Loss: 264.5624   time: 3.98s   best: 263.1782\n",
      "Epoch 234/500:  train Loss: 265.3420   val Loss: 266.0756   time: 3.98s   best: 263.1782\n",
      "Epoch 235/500:  train Loss: 265.1100   val Loss: 264.3090   time: 3.97s   best: 263.1782\n",
      "Epoch 236/500:  train Loss: 265.9662   val Loss: 267.6637   time: 3.97s   best: 263.1782\n",
      "Epoch 237/500:  train Loss: 265.1064   val Loss: 264.4525   time: 3.99s   best: 263.1782\n",
      "Epoch 238/500:  train Loss: 266.8909   val Loss: 264.5845   time: 4.02s   best: 263.1782\n",
      "Epoch 239/500:  train Loss: 266.6826   val Loss: 263.2163   time: 3.98s   best: 263.1782\n",
      "Epoch 240/500:  train Loss: 279.6575   val Loss: 267.8025   time: 3.98s   best: 263.1782\n",
      "Epoch 241/500:  train Loss: 269.7545   val Loss: 264.7406   time: 3.97s   best: 263.1782\n",
      "Epoch 242/500:  train Loss: 267.6698   val Loss: 264.4110   time: 3.97s   best: 263.1782\n",
      "Epoch 243/500:  train Loss: 265.8917   val Loss: 264.1022   time: 3.97s   best: 263.1782\n",
      "Epoch 244/500:  train Loss: 265.4428   val Loss: 263.2440   time: 4.03s   best: 263.1782\n",
      "Epoch 245/500:  train Loss: 264.3715   val Loss: 264.3258   time: 3.98s   best: 263.1782\n",
      "Epoch 246/500:  train Loss: 263.9079   val Loss: 264.2526   time: 3.98s   best: 263.1782\n",
      "Epoch 247/500:  train Loss: 264.1026   val Loss: 265.3374   time: 3.97s   best: 263.1782\n",
      "Epoch 248/500:  train Loss: 265.9520   val Loss: 264.1157   time: 3.97s   best: 263.1782\n",
      "Epoch 249/500:  train Loss: 265.3178   val Loss: 264.6231   time: 3.97s   best: 263.1782\n",
      "Epoch 250/500:  train Loss: 265.3897   val Loss: 265.4018   time: 4.02s   best: 263.1782\n",
      "Epoch 251/500:  train Loss: 266.0793   val Loss: 263.2710   time: 3.99s   best: 263.1782\n",
      "Epoch 252/500:  train Loss: 265.4884   val Loss: 264.6136   time: 3.98s   best: 263.1782\n",
      "Epoch 253/500:  train Loss: 264.8349   val Loss: 263.3261   time: 3.98s   best: 263.1782\n",
      "Epoch 254/500:  train Loss: 264.7137   val Loss: 264.2949   time: 3.97s   best: 263.1782\n",
      "Epoch 255/500:  train Loss: 268.4143   val Loss: 267.3447   time: 3.97s   best: 263.1782\n",
      "Epoch 256/500:  train Loss: 265.0189   val Loss: 265.5872   time: 3.98s   best: 263.1782\n",
      "Epoch 257/500:  train Loss: 264.3303   val Loss: 264.0120   time: 4.02s   best: 263.1782\n",
      "Epoch 258/500:  train Loss: 263.6534   val Loss: 265.3490   time: 3.98s   best: 263.1782\n",
      "Epoch 259/500:  train Loss: 263.8995   val Loss: 263.2204   time: 3.98s   best: 263.1782\n",
      "Epoch 260/500:  train Loss: 264.7899   val Loss: 264.0928   time: 3.97s   best: 263.1782\n",
      "Epoch 261/500:  train Loss: 264.5743   val Loss: 265.8984   time: 3.97s   best: 263.1782\n",
      "Epoch 262/500:  train Loss: 264.1431   val Loss: 265.4517   time: 3.96s   best: 263.1782\n",
      "Epoch 263/500:  train Loss: 264.6259   val Loss: 266.2595   time: 4.03s   best: 263.1782\n",
      "Epoch 264/500:  train Loss: 267.5812   val Loss: 266.3372   time: 3.98s   best: 263.1782\n",
      "Epoch 265/500:  train Loss: 264.5848   val Loss: 265.5543   time: 3.98s   best: 263.1782\n",
      "Epoch 266/500:  train Loss: 266.9927   val Loss: 264.6093   time: 3.97s   best: 263.1782\n",
      "Epoch 267/500:  train Loss: 267.8645   val Loss: 266.7198   time: 3.97s   best: 263.1782\n",
      "Epoch 268/500:  train Loss: 264.2247   val Loss: 264.3300   time: 3.97s   best: 263.1782\n",
      "Epoch 269/500:  train Loss: 265.5993   val Loss: 265.7470   time: 3.99s   best: 263.1782\n",
      "Epoch 270/500:  train Loss: 265.5630   val Loss: 267.8428   time: 4.00s   best: 263.1782\n",
      "Epoch 271/500:  train Loss: 267.2483   val Loss: 264.3091   time: 3.98s   best: 263.1782\n",
      "Epoch 272/500:  train Loss: 264.1483   val Loss: 264.0552   time: 3.98s   best: 263.1782\n",
      "Epoch 273/500:  train Loss: 263.8368   val Loss: 265.9785   time: 3.97s   best: 263.1782\n",
      "Epoch 274/500:  train Loss: 263.1591   val Loss: 264.4682   time: 3.97s   best: 263.1782\n",
      "Epoch 275/500:  train Loss: 264.3050   val Loss: 264.1294   time: 3.96s   best: 263.1782\n",
      "Epoch 276/500:  train Loss: 263.5619   val Loss: 265.1368   time: 4.03s   best: 263.1782\n",
      "Epoch 277/500:  train Loss: 263.4636   val Loss: 265.3427   time: 3.98s   best: 263.1782\n",
      "Epoch 278/500:  train Loss: 264.3246   val Loss: 265.0586   time: 3.98s   best: 263.1782\n",
      "Epoch 279/500:  train Loss: 266.1905   val Loss: 264.7917   time: 3.98s   best: 263.1782\n",
      "Epoch 280/500:  train Loss: 264.2902   val Loss: 267.1321   time: 3.97s   best: 263.1782\n",
      "Epoch 281/500:  train Loss: 282.8557   val Loss: 273.9724   time: 3.97s   best: 263.1782\n",
      "Epoch 282/500:  train Loss: 271.3964   val Loss: 264.9315   time: 4.01s   best: 263.1782\n",
      "Epoch 283/500:  train Loss: 266.7917   val Loss: 263.1595   time: 4.00s   best: 263.1595\n",
      "Epoch 284/500:  train Loss: 264.1822   val Loss: 264.1572   time: 3.98s   best: 263.1595\n",
      "Epoch 285/500:  train Loss: 264.7188   val Loss: 265.4555   time: 3.98s   best: 263.1595\n",
      "Epoch 286/500:  train Loss: 265.7314   val Loss: 264.2425   time: 3.96s   best: 263.1595\n",
      "Epoch 287/500:  train Loss: 263.8053   val Loss: 264.9328   time: 3.96s   best: 263.1595\n",
      "Epoch 288/500:  train Loss: 262.6095   val Loss: 263.5489   time: 3.97s   best: 263.1595\n",
      "Epoch 289/500:  train Loss: 262.4609   val Loss: 264.3099   time: 4.03s   best: 263.1595\n",
      "Epoch 290/500:  train Loss: 262.7218   val Loss: 264.1418   time: 3.98s   best: 263.1595\n",
      "Epoch 291/500:  train Loss: 263.5247   val Loss: 264.1614   time: 3.98s   best: 263.1595\n",
      "Epoch 292/500:  train Loss: 263.2608   val Loss: 264.4757   time: 3.97s   best: 263.1595\n",
      "Epoch 293/500:  train Loss: 268.4965   val Loss: 264.9067   time: 3.96s   best: 263.1595\n",
      "Epoch 294/500:  train Loss: 263.9700   val Loss: 263.5231   time: 3.95s   best: 263.1595\n",
      "Epoch 295/500:  train Loss: 263.1483   val Loss: 265.5115   time: 4.01s   best: 263.1595\n",
      "Epoch 296/500:  train Loss: 262.1116   val Loss: 263.8571   time: 3.97s   best: 263.1595\n",
      "Epoch 297/500:  train Loss: 263.8547   val Loss: 264.5614   time: 3.97s   best: 263.1595\n",
      "Epoch 298/500:  train Loss: 263.7625   val Loss: 264.8265   time: 3.96s   best: 263.1595\n",
      "Epoch 299/500:  train Loss: 265.7705   val Loss: 265.6556   time: 3.96s   best: 263.1595\n",
      "Epoch 300/500:  train Loss: 268.9771   val Loss: 264.7066   time: 3.96s   best: 263.1595\n",
      "Epoch 301/500:  train Loss: 264.9338   val Loss: 269.1387   time: 3.98s   best: 263.1595\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 302/500:  train Loss: 264.5235   val Loss: 264.5296   time: 4.00s   best: 263.1595\n",
      "Epoch 303/500:  train Loss: 264.5072   val Loss: 265.0196   time: 3.97s   best: 263.1595\n",
      "Epoch 304/500:  train Loss: 264.5574   val Loss: 267.8113   time: 3.97s   best: 263.1595\n",
      "Epoch 305/500:  train Loss: 267.6810   val Loss: 266.0417   time: 3.96s   best: 263.1595\n",
      "Epoch 306/500:  train Loss: 264.0913   val Loss: 263.3439   time: 3.97s   best: 263.1595\n",
      "Epoch 307/500:  train Loss: 268.8825   val Loss: 264.1446   time: 3.96s   best: 263.1595\n",
      "Epoch 308/500:  train Loss: 263.7318   val Loss: 263.8467   time: 4.03s   best: 263.1595\n",
      "Epoch 309/500:  train Loss: 262.8598   val Loss: 264.4201   time: 3.97s   best: 263.1595\n",
      "Epoch 310/500:  train Loss: 262.5566   val Loss: 263.4997   time: 3.97s   best: 263.1595\n",
      "Epoch 311/500:  train Loss: 263.5824   val Loss: 264.0809   time: 3.97s   best: 263.1595\n",
      "Epoch 312/500:  train Loss: 265.5168   val Loss: 264.1746   time: 3.96s   best: 263.1595\n",
      "Epoch 313/500:  train Loss: 267.9587   val Loss: 268.9791   time: 3.96s   best: 263.1595\n",
      "Epoch 314/500:  train Loss: 268.5691   val Loss: 265.7179   time: 3.99s   best: 263.1595\n",
      "Epoch 315/500:  train Loss: 264.3469   val Loss: 263.6947   time: 3.99s   best: 263.1595\n",
      "Epoch 316/500:  train Loss: 264.0141   val Loss: 267.1896   time: 3.97s   best: 263.1595\n",
      "Epoch 317/500:  train Loss: 263.5667   val Loss: 264.3816   time: 3.97s   best: 263.1595\n",
      "Epoch 318/500:  train Loss: 263.5069   val Loss: 265.3859   time: 3.96s   best: 263.1595\n",
      "Epoch 319/500:  train Loss: 266.3387   val Loss: 263.7335   time: 3.96s   best: 263.1595\n",
      "Epoch 320/500:  train Loss: 263.2026   val Loss: 263.7845   time: 3.96s   best: 263.1595\n",
      "Epoch 321/500:  train Loss: 263.0248   val Loss: 263.5674   time: 4.02s   best: 263.1595\n",
      "Epoch 322/500:  train Loss: 262.2181   val Loss: 264.4134   time: 3.97s   best: 263.1595\n",
      "Epoch 323/500:  train Loss: 264.8727   val Loss: 264.2440   time: 3.97s   best: 263.1595\n",
      "Epoch 324/500:  train Loss: 263.2916   val Loss: 265.4403   time: 3.96s   best: 263.1595\n",
      "Epoch 325/500:  train Loss: 264.7250   val Loss: 263.3557   time: 3.96s   best: 263.1595\n",
      "Epoch 326/500:  train Loss: 262.7763   val Loss: 263.6329   time: 3.96s   best: 263.1595\n",
      "Epoch 327/500:  train Loss: 262.3795   val Loss: 264.7600   time: 4.01s   best: 263.1595\n",
      "Epoch 328/500:  train Loss: 262.0533   val Loss: 265.5867   time: 3.97s   best: 263.1595\n",
      "Epoch 329/500:  train Loss: 264.8489   val Loss: 263.9878   time: 3.97s   best: 263.1595\n",
      "Epoch 330/500:  train Loss: 262.6234   val Loss: 265.6463   time: 3.96s   best: 263.1595\n",
      "Epoch 331/500:  train Loss: 262.6645   val Loss: 265.4036   time: 3.95s   best: 263.1595\n",
      "Epoch 332/500:  train Loss: 262.2967   val Loss: 264.5574   time: 3.96s   best: 263.1595\n",
      "Epoch 333/500:  train Loss: 262.7839   val Loss: 264.2924   time: 3.97s   best: 263.1595\n",
      "Epoch 334/500:  train Loss: 262.5366   val Loss: 264.5239   time: 4.01s   best: 263.1595\n",
      "Epoch 335/500:  train Loss: 268.0771   val Loss: 264.3774   time: 3.97s   best: 263.1595\n",
      "Epoch 336/500:  train Loss: 276.1908   val Loss: 288.1459   time: 3.97s   best: 263.1595\n",
      "Epoch 337/500:  train Loss: 274.6271   val Loss: 264.1681   time: 3.96s   best: 263.1595\n",
      "Epoch 338/500:  train Loss: 264.0572   val Loss: 263.8851   time: 3.96s   best: 263.1595\n",
      "Epoch 339/500:  train Loss: 262.8213   val Loss: 263.8512   time: 3.95s   best: 263.1595\n",
      "Epoch 340/500:  train Loss: 263.0094   val Loss: 263.1805   time: 4.02s   best: 263.1595\n",
      "Epoch 341/500:  train Loss: 262.0553   val Loss: 264.6290   time: 3.97s   best: 263.1595\n",
      "Epoch 342/500:  train Loss: 262.5730   val Loss: 264.6615   time: 3.96s   best: 263.1595\n",
      "Epoch 343/500:  train Loss: 262.1035   val Loss: 263.6804   time: 3.97s   best: 263.1595\n",
      "Epoch 344/500:  train Loss: 262.4947   val Loss: 265.0667   time: 3.97s   best: 263.1595\n",
      "Epoch 345/500:  train Loss: 268.0575   val Loss: 264.1127   time: 3.96s   best: 263.1595\n",
      "Epoch 346/500:  train Loss: 262.5029   val Loss: 262.6726   time: 4.01s   best: 262.6726\n",
      "Epoch 347/500:  train Loss: 265.2282   val Loss: 265.1336   time: 4.00s   best: 262.6726\n",
      "Epoch 348/500:  train Loss: 263.8324   val Loss: 263.5153   time: 3.99s   best: 262.6726\n",
      "Epoch 349/500:  train Loss: 263.6299   val Loss: 263.1324   time: 3.99s   best: 262.6726\n",
      "Epoch 350/500:  train Loss: 263.7785   val Loss: 270.5539   time: 3.98s   best: 262.6726\n",
      "Epoch 351/500:  train Loss: 263.1575   val Loss: 264.6383   time: 3.97s   best: 262.6726\n",
      "Epoch 352/500:  train Loss: 262.9512   val Loss: 262.8040   time: 3.97s   best: 262.6726\n",
      "Epoch 353/500:  train Loss: 261.4415   val Loss: 264.5895   time: 4.02s   best: 262.6726\n",
      "Epoch 354/500:  train Loss: 260.7317   val Loss: 263.2035   time: 3.98s   best: 262.6726\n",
      "Epoch 355/500:  train Loss: 262.7381   val Loss: 264.4017   time: 3.98s   best: 262.6726\n",
      "Epoch 356/500:  train Loss: 263.8528   val Loss: 265.7309   time: 3.97s   best: 262.6726\n",
      "Epoch 357/500:  train Loss: 262.6672   val Loss: 264.3808   time: 3.97s   best: 262.6726\n",
      "Epoch 358/500:  train Loss: 263.5617   val Loss: 267.0939   time: 3.96s   best: 262.6726\n",
      "Epoch 359/500:  train Loss: 264.6494   val Loss: 263.8694   time: 4.03s   best: 262.6726\n",
      "Epoch 360/500:  train Loss: 263.2548   val Loss: 264.8130   time: 3.97s   best: 262.6726\n",
      "Epoch 361/500:  train Loss: 262.2594   val Loss: 265.3800   time: 3.97s   best: 262.6726\n",
      "Epoch 362/500:  train Loss: 262.6458   val Loss: 264.3990   time: 3.97s   best: 262.6726\n",
      "Epoch 363/500:  train Loss: 262.0910   val Loss: 264.0344   time: 3.96s   best: 262.6726\n",
      "Epoch 364/500:  train Loss: 261.5838   val Loss: 264.8521   time: 3.97s   best: 262.6726\n",
      "Epoch 365/500:  train Loss: 263.8142   val Loss: 263.5353   time: 4.00s   best: 262.6726\n",
      "Epoch 366/500:  train Loss: 263.0069   val Loss: 263.2550   time: 4.00s   best: 262.6726\n",
      "Epoch 367/500:  train Loss: 262.8106   val Loss: 275.5220   time: 3.98s   best: 262.6726\n",
      "Epoch 368/500:  train Loss: 267.6559   val Loss: 264.4972   time: 3.98s   best: 262.6726\n",
      "Epoch 369/500:  train Loss: 265.0406   val Loss: 264.2942   time: 3.96s   best: 262.6726\n",
      "Epoch 370/500:  train Loss: 262.1314   val Loss: 267.7274   time: 3.97s   best: 262.6726\n",
      "Epoch 371/500:  train Loss: 262.3849   val Loss: 263.0547   time: 3.97s   best: 262.6726\n",
      "Epoch 372/500:  train Loss: 265.1660   val Loss: 262.6482   time: 4.02s   best: 262.6482\n",
      "Epoch 373/500:  train Loss: 261.2114   val Loss: 263.8186   time: 3.98s   best: 262.6482\n",
      "Epoch 374/500:  train Loss: 262.6762   val Loss: 263.8323   time: 3.97s   best: 262.6482\n",
      "Epoch 375/500:  train Loss: 261.0726   val Loss: 264.8130   time: 3.96s   best: 262.6482\n",
      "Epoch 376/500:  train Loss: 261.8624   val Loss: 263.2408   time: 3.96s   best: 262.6482\n",
      "Epoch 377/500:  train Loss: 263.4700   val Loss: 263.0201   time: 3.95s   best: 262.6482\n",
      "Epoch 378/500:  train Loss: 262.1096   val Loss: 263.9210   time: 4.02s   best: 262.6482\n",
      "Epoch 379/500:  train Loss: 261.3821   val Loss: 265.7259   time: 3.97s   best: 262.6482\n",
      "Epoch 380/500:  train Loss: 262.2714   val Loss: 264.9716   time: 3.97s   best: 262.6482\n",
      "Epoch 381/500:  train Loss: 260.9510   val Loss: 263.9391   time: 3.97s   best: 262.6482\n",
      "Epoch 382/500:  train Loss: 262.1758   val Loss: 265.6451   time: 3.96s   best: 262.6482\n",
      "Epoch 383/500:  train Loss: 262.2115   val Loss: 264.1942   time: 3.96s   best: 262.6482\n",
      "Epoch 384/500:  train Loss: 260.6796   val Loss: 263.5254   time: 3.98s   best: 262.6482\n",
      "Epoch 385/500:  train Loss: 267.5613   val Loss: 263.4799   time: 4.00s   best: 262.6482\n",
      "Epoch 386/500:  train Loss: 262.5074   val Loss: 263.2849   time: 3.97s   best: 262.6482\n",
      "Epoch 387/500:  train Loss: 261.3326   val Loss: 263.1706   time: 3.97s   best: 262.6482\n",
      "Epoch 388/500:  train Loss: 260.9474   val Loss: 265.1102   time: 3.96s   best: 262.6482\n",
      "Epoch 389/500:  train Loss: 261.6869   val Loss: 263.2142   time: 3.96s   best: 262.6482\n",
      "Epoch 390/500:  train Loss: 268.5667   val Loss: 265.4096   time: 3.96s   best: 262.6482\n",
      "Epoch 391/500:  train Loss: 264.0308   val Loss: 262.9985   time: 4.02s   best: 262.6482\n",
      "Epoch 392/500:  train Loss: 261.7670   val Loss: 263.7021   time: 3.97s   best: 262.6482\n",
      "Epoch 393/500:  train Loss: 260.6933   val Loss: 264.1058   time: 3.97s   best: 262.6482\n",
      "Epoch 394/500:  train Loss: 261.9642   val Loss: 263.4177   time: 3.97s   best: 262.6482\n",
      "Epoch 395/500:  train Loss: 260.3444   val Loss: 263.0853   time: 3.96s   best: 262.6482\n",
      "Epoch 396/500:  train Loss: 262.8389   val Loss: 264.0459   time: 3.96s   best: 262.6482\n",
      "Epoch 397/500:  train Loss: 261.1771   val Loss: 265.0364   time: 4.01s   best: 262.6482\n",
      "Epoch 398/500:  train Loss: 260.5919   val Loss: 265.5205   time: 3.98s   best: 262.6482\n",
      "Epoch 399/500:  train Loss: 260.7024   val Loss: 264.4023   time: 3.98s   best: 262.6482\n",
      "Epoch 400/500:  train Loss: 260.6335   val Loss: 264.4031   time: 3.97s   best: 262.6482\n",
      "Epoch 401/500:  train Loss: 261.2724   val Loss: 265.0568   time: 3.97s   best: 262.6482\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 402/500:  train Loss: 260.9300   val Loss: 265.8426   time: 3.97s   best: 262.6482\n",
      "Epoch 403/500:  train Loss: 260.6139   val Loss: 265.0009   time: 3.98s   best: 262.6482\n",
      "Epoch 404/500:  train Loss: 260.7138   val Loss: 262.7832   time: 4.02s   best: 262.6482\n",
      "Epoch 405/500:  train Loss: 261.3542   val Loss: 264.6556   time: 3.98s   best: 262.6482\n",
      "Epoch 406/500:  train Loss: 261.6757   val Loss: 263.4214   time: 3.97s   best: 262.6482\n",
      "Epoch 407/500:  train Loss: 262.1128   val Loss: 263.5249   time: 3.96s   best: 262.6482\n",
      "Epoch 408/500:  train Loss: 263.6259   val Loss: 264.6792   time: 3.96s   best: 262.6482\n",
      "Epoch 409/500:  train Loss: 261.0830   val Loss: 265.6620   time: 3.96s   best: 262.6482\n",
      "Epoch 410/500:  train Loss: 260.5571   val Loss: 264.8511   time: 4.01s   best: 262.6482\n",
      "Epoch 411/500:  train Loss: 261.0039   val Loss: 263.4404   time: 3.98s   best: 262.6482\n",
      "Epoch 412/500:  train Loss: 261.4286   val Loss: 265.2947   time: 3.97s   best: 262.6482\n",
      "Epoch 413/500:  train Loss: 260.4969   val Loss: 264.6440   time: 3.97s   best: 262.6482\n",
      "Epoch 414/500:  train Loss: 261.0239   val Loss: 263.8412   time: 3.96s   best: 262.6482\n",
      "Epoch 415/500:  train Loss: 264.9151   val Loss: 267.3171   time: 3.97s   best: 262.6482\n",
      "Epoch 416/500:  train Loss: 261.7454   val Loss: 263.7256   time: 3.98s   best: 262.6482\n",
      "Epoch 417/500:  train Loss: 261.3960   val Loss: 264.6450   time: 4.01s   best: 262.6482\n",
      "Epoch 418/500:  train Loss: 260.0309   val Loss: 264.5673   time: 3.98s   best: 262.6482\n",
      "Epoch 419/500:  train Loss: 259.5723   val Loss: 265.8711   time: 3.98s   best: 262.6482\n",
      "Epoch 420/500:  train Loss: 259.9954   val Loss: 264.5960   time: 3.97s   best: 262.6482\n",
      "Epoch 421/500:  train Loss: 261.7549   val Loss: 263.5556   time: 3.97s   best: 262.6482\n",
      "Epoch 422/500:  train Loss: 261.4302   val Loss: 263.9435   time: 3.97s   best: 262.6482\n",
      "Epoch 423/500:  train Loss: 260.9041   val Loss: 266.2807   time: 4.03s   best: 262.6482\n",
      "Epoch 424/500:  train Loss: 264.6939   val Loss: 262.9417   time: 3.97s   best: 262.6482\n",
      "Epoch 425/500:  train Loss: 260.8249   val Loss: 264.2765   time: 3.97s   best: 262.6482\n",
      "Epoch 426/500:  train Loss: 260.6298   val Loss: 264.2532   time: 3.97s   best: 262.6482\n",
      "Epoch 427/500:  train Loss: 263.7441   val Loss: 262.8967   time: 3.96s   best: 262.6482\n",
      "Epoch 428/500:  train Loss: 266.5348   val Loss: 264.1105   time: 3.96s   best: 262.6482\n",
      "Epoch 429/500:  train Loss: 261.1851   val Loss: 263.1084   time: 4.01s   best: 262.6482\n",
      "Epoch 430/500:  train Loss: 260.1318   val Loss: 262.5647   time: 3.99s   best: 262.5647\n",
      "Epoch 431/500:  train Loss: 259.0970   val Loss: 263.0874   time: 3.98s   best: 262.5647\n",
      "Epoch 432/500:  train Loss: 260.0072   val Loss: 263.2235   time: 3.98s   best: 262.5647\n",
      "Epoch 433/500:  train Loss: 259.4189   val Loss: 264.0946   time: 3.97s   best: 262.5647\n",
      "Epoch 434/500:  train Loss: 259.7526   val Loss: 262.4243   time: 3.97s   best: 262.4243\n",
      "Epoch 435/500:  train Loss: 259.7494   val Loss: 262.5525   time: 3.97s   best: 262.4243\n",
      "Epoch 436/500:  train Loss: 261.6098   val Loss: 263.7991   time: 4.01s   best: 262.4243\n",
      "Epoch 437/500:  train Loss: 261.7371   val Loss: 263.6957   time: 3.97s   best: 262.4243\n",
      "Epoch 438/500:  train Loss: 261.9935   val Loss: 263.5479   time: 3.97s   best: 262.4243\n",
      "Epoch 439/500:  train Loss: 260.5912   val Loss: 263.6602   time: 3.96s   best: 262.4243\n",
      "Epoch 440/500:  train Loss: 260.8789   val Loss: 264.9711   time: 3.96s   best: 262.4243\n",
      "Epoch 441/500:  train Loss: 260.1106   val Loss: 265.0170   time: 3.95s   best: 262.4243\n",
      "Epoch 442/500:  train Loss: 259.4969   val Loss: 264.2191   time: 4.02s   best: 262.4243\n",
      "Epoch 443/500:  train Loss: 259.7763   val Loss: 263.1680   time: 3.97s   best: 262.4243\n",
      "Epoch 444/500:  train Loss: 262.2697   val Loss: 263.2930   time: 3.97s   best: 262.4243\n",
      "Epoch 445/500:  train Loss: 259.8809   val Loss: 263.0340   time: 3.97s   best: 262.4243\n",
      "Epoch 446/500:  train Loss: 259.8005   val Loss: 261.6114   time: 3.97s   best: 261.6114\n",
      "Epoch 447/500:  train Loss: 259.7868   val Loss: 264.3419   time: 3.97s   best: 261.6114\n",
      "Epoch 448/500:  train Loss: 259.5156   val Loss: 264.3945   time: 3.99s   best: 261.6114\n",
      "Epoch 449/500:  train Loss: 259.8697   val Loss: 263.9579   time: 4.00s   best: 261.6114\n",
      "Epoch 450/500:  train Loss: 260.8902   val Loss: 265.8199   time: 3.98s   best: 261.6114\n",
      "Epoch 451/500:  train Loss: 260.8038   val Loss: 265.0108   time: 3.98s   best: 261.6114\n",
      "Epoch 452/500:  train Loss: 261.0187   val Loss: 264.1153   time: 3.96s   best: 261.6114\n",
      "Epoch 453/500:  train Loss: 258.8501   val Loss: 263.5506   time: 3.97s   best: 261.6114\n",
      "Epoch 454/500:  train Loss: 264.1203   val Loss: 271.0507   time: 3.96s   best: 261.6114\n",
      "Epoch 455/500:  train Loss: 263.6735   val Loss: 263.9753   time: 4.02s   best: 261.6114\n",
      "Epoch 456/500:  train Loss: 260.3531   val Loss: 264.5757   time: 3.98s   best: 261.6114\n",
      "Epoch 457/500:  train Loss: 259.2927   val Loss: 263.0047   time: 3.97s   best: 261.6114\n",
      "Epoch 458/500:  train Loss: 258.8673   val Loss: 264.1424   time: 3.97s   best: 261.6114\n",
      "Epoch 459/500:  train Loss: 260.5030   val Loss: 263.3838   time: 3.96s   best: 261.6114\n",
      "Epoch 460/500:  train Loss: 260.8670   val Loss: 264.6676   time: 3.96s   best: 261.6114\n",
      "Epoch 461/500:  train Loss: 269.1219   val Loss: 264.4208   time: 4.02s   best: 261.6114\n",
      "Epoch 462/500:  train Loss: 264.2823   val Loss: 262.7926   time: 3.98s   best: 261.6114\n",
      "Epoch 463/500:  train Loss: 260.7528   val Loss: 262.6788   time: 3.97s   best: 261.6114\n",
      "Epoch 464/500:  train Loss: 260.0853   val Loss: 263.0420   time: 3.97s   best: 261.6114\n",
      "Epoch 465/500:  train Loss: 258.7830   val Loss: 264.3099   time: 3.96s   best: 261.6114\n",
      "Epoch 466/500:  train Loss: 259.8420   val Loss: 267.4215   time: 3.96s   best: 261.6114\n",
      "Epoch 467/500:  train Loss: 261.6197   val Loss: 264.0816   time: 3.98s   best: 261.6114\n",
      "Epoch 468/500:  train Loss: 259.2667   val Loss: 264.9808   time: 4.01s   best: 261.6114\n",
      "Epoch 469/500:  train Loss: 259.4267   val Loss: 264.3454   time: 3.98s   best: 261.6114\n",
      "Epoch 470/500:  train Loss: 258.1884   val Loss: 262.7119   time: 3.98s   best: 261.6114\n",
      "Epoch 471/500:  train Loss: 258.9817   val Loss: 262.4477   time: 3.97s   best: 261.6114\n",
      "Epoch 472/500:  train Loss: 259.5791   val Loss: 263.3130   time: 3.97s   best: 261.6114\n",
      "Epoch 473/500:  train Loss: 258.5606   val Loss: 263.4206   time: 3.96s   best: 261.6114\n",
      "Epoch 474/500:  train Loss: 260.9714   val Loss: 264.4265   time: 4.02s   best: 261.6114\n",
      "Epoch 475/500:  train Loss: 259.6487   val Loss: 262.8172   time: 3.97s   best: 261.6114\n",
      "Epoch 476/500:  train Loss: 259.8785   val Loss: 265.1720   time: 3.97s   best: 261.6114\n",
      "Epoch 477/500:  train Loss: 258.7543   val Loss: 263.6563   time: 3.96s   best: 261.6114\n",
      "Epoch 478/500:  train Loss: 259.7296   val Loss: 263.8905   time: 3.96s   best: 261.6114\n",
      "Epoch 479/500:  train Loss: 259.1315   val Loss: 264.1780   time: 3.96s   best: 261.6114\n",
      "Epoch 480/500:  train Loss: 261.6028   val Loss: 264.9346   time: 4.01s   best: 261.6114\n",
      "Epoch 481/500:  train Loss: 261.1076   val Loss: 264.4875   time: 3.98s   best: 261.6114\n",
      "Epoch 482/500:  train Loss: 259.3896   val Loss: 263.7392   time: 3.97s   best: 261.6114\n",
      "Epoch 483/500:  train Loss: 259.8932   val Loss: 263.0583   time: 3.97s   best: 261.6114\n",
      "Epoch 484/500:  train Loss: 260.4680   val Loss: 263.1448   time: 3.96s   best: 261.6114\n",
      "Epoch 485/500:  train Loss: 262.6726   val Loss: 264.4945   time: 3.96s   best: 261.6114\n",
      "Epoch 486/500:  train Loss: 259.2686   val Loss: 263.9619   time: 3.97s   best: 261.6114\n",
      "Epoch 487/500:  train Loss: 258.3374   val Loss: 263.3708   time: 4.01s   best: 261.6114\n",
      "Epoch 488/500:  train Loss: 259.4038   val Loss: 262.9415   time: 3.98s   best: 261.6114\n",
      "Epoch 489/500:  train Loss: 263.4038   val Loss: 265.9775   time: 3.97s   best: 261.6114\n",
      "Epoch 490/500:  train Loss: 260.3952   val Loss: 262.7121   time: 3.97s   best: 261.6114\n",
      "Epoch 491/500:  train Loss: 259.5421   val Loss: 263.4040   time: 3.97s   best: 261.6114\n",
      "Epoch 492/500:  train Loss: 258.0367   val Loss: 262.6095   time: 3.96s   best: 261.6114\n",
      "Epoch 493/500:  train Loss: 259.7257   val Loss: 264.2567   time: 4.03s   best: 261.6114\n",
      "Epoch 494/500:  train Loss: 260.4467   val Loss: 263.1309   time: 3.98s   best: 261.6114\n",
      "Epoch 495/500:  train Loss: 258.8690   val Loss: 263.3839   time: 3.98s   best: 261.6114\n",
      "Epoch 496/500:  train Loss: 259.0095   val Loss: 263.1472   time: 3.98s   best: 261.6114\n",
      "Epoch 497/500:  train Loss: 258.5081   val Loss: 263.3901   time: 3.97s   best: 261.6114\n",
      "Epoch 498/500:  train Loss: 261.6600   val Loss: 263.5697   time: 3.97s   best: 261.6114\n",
      "Epoch 499/500:  train Loss: 262.5547   val Loss: 265.3055   time: 3.99s   best: 261.6114\n",
      "Epoch 500/500:  train Loss: 262.1683   val Loss: 263.3850   time: 4.00s   best: 261.6114\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete in 33m 10s   best validation loss: 261.6114\n"
     ]
    }
   ],
   "source": [
    "bl_dict={'train':train_bl, 'val':val_bl}\n",
    "log_file = 'log_test1.txt'\n",
    "\n",
    "losses, best_model = train_model(model, criterion, optimizer , max_lr=0.001, dataloader=bl_dict,\n",
    "                                 num_epochs=500, logFile=log_file, log_every=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), 'Models/full_1000_0.001_22_NewDS_Reduced_Fusion_2000_Fixed_Final.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load existing model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# model.load_state_dict(torch.load('Models/full_1000_0.001_106_13M115_dropout_fused_kBest_0.0006_2000_4.pkl',map_location=train_device))\n",
    "model.load_state_dict(torch.load('Models/full_1000_0.001_106_13M115_dropout_fused_kBest_0.001_2000_ALL+New_0.2.pkl',map_location=train_device))\n",
    "model.to(train_device)\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#accuracy 1st-shot, 2-shots, 3-shots and 5-shots\n",
    "def accuracy(df):\n",
    "    #return np.sum(df[\"prediction\"] == df[\"target\"]) / len(df) * 100\n",
    "    targets = np.array(df['target'].values.tolist()) # 1-dim array (#rows, ) = list\n",
    "    #we went to lists and back to get the last dimension of the (#rows, #size_output) instead of (#rows,)\n",
    "    predictions = np.array(df['prediction'].values.tolist())\n",
    "    \n",
    "    first = np.argmax(targets[:,0:one_output],-1) == np.argmax(predictions[:,0:one_output],-1) \n",
    "    second = np.argmax(targets[:,0:one_output],-1) ==  np.argmax(predictions[:,one_output:one_output+one_output],-1)\n",
    "    third = np.argmax(targets[:,0:one_output],-1) ==  np.argmax(predictions[:,one_output*2:one_output*3],-1)\n",
    "    forth = np.argmax(targets[:,0:one_output],-1) ==  np.argmax(predictions[:,one_output*3:one_output*4],-1 )  \n",
    "    fifth = np.argmax(targets[:,0:one_output],-1) ==  np.argmax(predictions[:,one_output*4:one_output*5],-1)\n",
    "    firstorsecond = np.logical_or(first,second)\n",
    "    firstorsecondorsthird = firstorsecond | third # same as logical or\n",
    "    anywhere = firstorsecondorsthird | forth | fifth\n",
    "    return np.sum(first == True) / len(df) * 100, np.sum(firstorsecond == True) / len(df) * 100, np.sum(firstorsecondorsthird == True) / len(df) * 100, np.sum(anywhere == True) / len(df) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
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      " 71%|███████▏  | 261/366 [00:01<00:00, 227.14it/s]"
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "#get results of the model on the datasets previously loaded\n",
    "%run utils_k-best.py\n",
    "val_df = get_results_df(train_val_dataset, val_bl, val_indices, model)\n",
    "test_df = get_results_df(test_dataset, test_bl, test_indices, model)\n",
    "train_df = get_results_df(train_val_dataset, train_bl, train_indices, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train (80.80645926046344, 92.9419574214437, 95.52268124335345, 96.80626960096997) val (75.54317548746519, 90.89003846664014, 94.75261971083698, 96.67064597426715) test (80.55928253654403, 94.88736671139044, 97.37306687380834, 98.20634136007345)\n"
     ]
    }
   ],
   "source": [
    "#print the accuracies of the model on each dataset.\n",
    "print( \"train\", accuracy(train_df), \"val\", accuracy(val_df), \"test\", accuracy(test_df) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [00:00<00:00, 348.33it/s]\n",
      "100%|██████████| 3/3 [00:00<00:00, 479.57it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of batches 3\n",
      "Data loaded\n",
      "Sizes: (3, 0) batches\n",
      "torch.Size([10, 110]) torch.Size([10, 110])\n",
      "torch.Size([16, 110]) torch.Size([16, 110])\n",
      "torch.Size([4, 110]) torch.Size([4, 110])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%run utils_k-best.py\n",
    "\n",
    "# single computations 'real' benchmark\n",
    "benchmark_dataset_file = '/data/mm12191/datasets/benchmarks_ds2.json'\n",
    "bench_ds, bench_bl, bench_indices, _, _ = load_merge_data(None, benchmark_dataset_file, 1, filter_func_MC=filter_schedule_MC, filter_func_SC=filter_schedule_SC)\n",
    "bench_df = get_results_df(bench_ds, bench_bl, bench_indices, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "bench_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(76.66666666666667, 93.33333333333333, 100.0, 100.0)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accuracy on the chosen benchmark (1st shot, 2-shots, 3-shots and 5-shots (any shot))\n",
    "accuracy(bench_df) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 58%|█████▊    | 95/163 [00:00<00:00, 296.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F({C0,C1},L4) function190298\n",
      "F({C1,C2},L4) function190359\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 163/163 [00:00<00:00, 313.03it/s]\n",
      "100%|██████████| 32/32 [00:00<00:00, 298.11it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of batches 32\n",
      "Number of batches dropped due to too much memory accesses:5\n",
      "Data loaded\n",
      "Sizes: (32, 0) batches\n",
      "torch.Size([1, 110]) torch.Size([1, 110])\n",
      "torch.Size([50, 110]) torch.Size([50, 110])\n",
      "torch.Size([2, 110]) torch.Size([2, 110])\n",
      "torch.Size([1, 110]) torch.Size([1, 110])\n",
      "torch.Size([1, 110]) torch.Size([1, 110])\n",
      "torch.Size([1, 110]) torch.Size([1, 110])\n",
      "torch.Size([1, 110]) torch.Size([1, 110])\n",
      "torch.Size([2, 110]) torch.Size([2, 110])\n",
      "torch.Size([1, 110]) torch.Size([1, 110])\n",
      "torch.Size([1, 110]) torch.Size([1, 110])\n",
      "torch.Size([1, 110]) torch.Size([1, 110])\n",
      "torch.Size([1, 110]) torch.Size([1, 110])\n",
      "torch.Size([1, 110]) torch.Size([1, 110])\n",
      "torch.Size([1, 110]) torch.Size([1, 110])\n",
      "torch.Size([1, 110]) torch.Size([1, 110])\n",
      "torch.Size([1, 110]) torch.Size([1, 110])\n",
      "torch.Size([1, 110]) torch.Size([1, 110])\n",
      "torch.Size([34, 110]) torch.Size([34, 110])\n",
      "torch.Size([2, 110]) torch.Size([2, 110])\n",
      "torch.Size([1, 110]) torch.Size([1, 110])\n",
      "torch.Size([4, 110]) torch.Size([4, 110])\n",
      "torch.Size([2, 110]) torch.Size([2, 110])\n",
      "torch.Size([1, 110]) torch.Size([1, 110])\n",
      "torch.Size([3, 110]) torch.Size([3, 110])\n",
      "torch.Size([1, 110]) torch.Size([1, 110])\n",
      "torch.Size([14, 110]) torch.Size([14, 110])\n",
      "torch.Size([2, 110]) torch.Size([2, 110])\n",
      "torch.Size([1, 110]) torch.Size([1, 110])\n",
      "torch.Size([3, 110]) torch.Size([3, 110])\n",
      "torch.Size([1, 110]) torch.Size([1, 110])\n",
      "torch.Size([22, 110]) torch.Size([22, 110])\n",
      "torch.Size([1, 110]) torch.Size([1, 110])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# #long programs synthetic test set.\n",
    "benchmark_dataset_file = '/data/mm12191/datasets/dataset_batch190201-190710.pkl'\n",
    "bench_ds, bench_bl, bench_indices, _, _ = load_merge_data(benchmark_dataset_file, None, 1, filter_func_MC=filter_schedule_MC, filter_func_SC=filter_schedule_SC)\n",
    "bench_df = get_results_df(bench_ds, bench_bl, bench_indices, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49.375, 67.5, 73.125, 77.5)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accuracy on the chosen benchmark (1st shot, 2-shots, 3-shots and 5-shots (any shot))\n",
    "accuracy(bench_df) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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